Dynamic similarity promotes interpersonal coordination in joint action

Piotr Słowiński, Chao Zhai, Francesco Alderisio, Robin Salesse, Mathieu Gueugnon, Ludovic Marin, Benoit G. Bardy, Mario Di Bernardo, Krasimira Tsaneva-Atanasova*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

41 Citations (Scopus)
406 Downloads (Pure)

Abstract

Human movement has been studied for decades, and dynamic laws of motion that are common to all humans have been derived. Yet, every individual moves differently from everyone else (faster/slower, harder/smoother, etc.). We propose here an index of such variability, namely an individual motor signature (IMS) able to capture the subtle differences in the way each of us moves. We show that the IMS of a person is time-invariant and that it significantly differs from those of other individuals. This allows us to quantify the dynamic similarity, a measure of rapport between dynamics of different individuals’ movements, and demonstrate that it facilitates coordination during interaction. We use our measure to confirm a key prediction of the theory of similarity that coordination between two individuals performing a joint-action task is higher if their motions share similar dynamic features. Furthermore, we use a virtual avatar driven by an interactive cognitive architecture based on feedback control theory to explore the effects of different kinematic features of the avatar motion on coordination with human players.
Original languageEnglish
Article number20151093
Number of pages11
JournalJournal of the Royal Society Interface
Volume13
Issue number116
Early online date23 Mar 2016
DOIs
Publication statusPublished - 31 Mar 2016

Keywords

  • movement dynamics
  • statistical analysis
  • mathematical modelling

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